A Review of Real-Time Strategy Game AI

Authors

  • Glen Robertson University of Aukland
  • Ian Watson University of Auckland

DOI:

https://doi.org/10.1609/aimag.v35i4.2478

Keywords:

Game AI, Case-Based Reasoning, Reinforcement Learning, Game-Tree Search, Cognitive Architectures

Abstract

This literature review covers AI techniques used for real-time strategy video games, focusing specifically on StarCraft. It finds that the main areas of current academic research are in tactical and strategic decision-making, plan recognition, and learning, and it outlines the research contributions in each of these areas. The paper then contrasts the use of game AI in academia and industry, finding the academic research heavily focused on creating game-winning agents, while the indus- try aims to maximise player enjoyment. It finds the industry adoption of academic research is low because it is either in- applicable or too time-consuming and risky to implement in a new game, which highlights an area for potential investi- gation: bridging the gap between academia and industry. Fi- nally, the areas of spatial reasoning, multi-scale AI, and co- operation are found to require future work, and standardised evaluation methods are proposed to produce comparable re- sults between studies.

Author Biographies

Glen Robertson, University of Aukland

Department of Computer Science, PhD student

Ian Watson, University of Auckland

Department of Computer Science, Assoc. Prof. of Artificial Intelligence
AI Magazine Winter Issue

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Published

2014-12-22

How to Cite

Robertson, G., & Watson, I. (2014). A Review of Real-Time Strategy Game AI. AI Magazine, 35(4), 75-104. https://doi.org/10.1609/aimag.v35i4.2478

Issue

Vol. 35 No. 4: Winter 2014

Section

Articles

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